Pseudo-genetic meta-knowledge artificial intelligence systems and methods

ABSTRACT

Systems and methods for artificially intelligent physical and virtual actors including pseudo-genetic information and configured to retain meta-knowledge are described. The actors are adapted with unique behavioral and physical attributes which may independently evolve over time. The attributes may include reproduction based attributes which direct how, when, and with whom the actor reproduces and may be subject to mutation and reproductive forces. The attributes may include evaluation attributes which dictate when and how each actor evaluates their performance. The evaluation attributes may also be subject to mutation and reproductive forces. The attributes may consider meta-knowledge which is a reflection of information gathered by an actor. What and how much meta-knowledge is collected may also be expressed as one or more evolvable attributes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and is a continuation of U.S.application Ser. No. 13/462,683, filed on May 2, 2012, entitledPSEUDO-GENETIC META-KNOWLEDGE ARTIFICIAL INTELLIGENCE SYSTEMS ANDMETHODS, which is hereby incorporated herein by reference in itsentirety. Any and all priority claims identified in the Application DataSheet, or any correction thereto, are hereby incorporated by referenceunder 37 C.F.R. §1.57.

BACKGROUND

1. Field

The present application relates generally to artificial intelligencesystems and methods, and, more specifically, to pseudo-geneticmeta-knowledge based artificial intelligence systems and methods.

2. Background

Artificial intelligence systems seek to provide human-like behavior toautonomous actors. An actor may be an entity programmed to interact inan environment. An actor can be anything as simple as a computeropponent in online poker (simulated environment), or a drone executing acomplex series of military maneuvers (real environment). To produceactors, some systems include genetic algorithms. Genetic algorithms mayinclude Boolean variables to express hypothetical actors. Each of theseBoolean variables may be considered a “gene.” When two parents “mate” toform offspring, their children may inherit Boolean “genes” as determinedby a global “Genetic Operator” function.

Some systems include genetic programming Genetic programming generallyrefers to algorithms which use mathematical functions to express ahypothesis. These mathematical functions may be created byever-elongating chains of operators, static numbers, and variables—eachof which is considered a “gene.” When parents “mate” to form offspring,their children may inherit “chains” of mathematical operations asdetermined by a global “Genetic Operator” function.

A genetic operator generally refers to a “mating” operation which mixesthe genetic information from genetic algorithm or genetic programmingparents into their offspring. This is a static, hard-coded function atthe start of the simulation. Specific examples of “mating” operationsinclude: single-point crossover, two-point crossover, uniform crossover,and point mutation.

The genetic algorithms and genetic programs hold physical attributesstatic while the artificial intelligence is permitted to evolve overtime. Accordingly, all actors have the same physical characteristics.Thus, the actors generated through these methods are a reflection ofbehavior evolution which may be tied to a specific physical form.

Furthermore, the evolution process genetic algorithms and geneticprograms evaluate actors based on a fixed learning parameter. Forexample, reward functions may be chosen at the time of programming, andare assigned a static weight function by the simulation designer. Areward function generally refers to an immediate, quantitativelymeasurable result obtained by an actor based on its environment. Forexample, an actor used for blackjack card game simulation could have itsreward function consider the dollar amount won in every game. Rewardfunction values in an infinite sample may also attenuate over time, butonly as dictated by a single depreciation variable. The simulationdesigner may select weights for the reward functions and depreciationvariables which appear to be desirable, but in practice do not generatethe optimal results in a given environment. This can only be discoveredthrough thousands of simulated experiments and results, and can beprohibitively time consuming.

In addition, many reward functions rely on the Markov Property (MP) orMarkov Decision Process (MDP), which presumes that historical states oractions do not impact current or future decisions. This may limit theirsimulation's ability to recognize stagnation in macroscopic behaviorpatterns which can be exploited by observant humans.

In many systems, the reward function may determine which actors areselected for reproduction, and which are culled from the testing pool.The timing for reproduction is generally universal for all actors, thatis, the simulation allocates a time for reproduction. While this canlead to rapid convergence to a set of successful behaviors, it can alsolead to rapid homogenization of a population. This mass homogenizationmay create a universal weakness in the actors when a single exploit isfound.

Accordingly, improved artificial intelligence systems and methods aredesirable.

SUMMARY

The methods and devices described each have several aspects, no singleone of which is solely responsible for its desirable attributes. Withoutlimiting the scope of this disclosure, some features will now bediscussed briefly. After considering this discussion, and particularlyafter reading the section entitled “Detailed Description” one willunderstand how the features described provide advantages that includeartificial intelligence systems and methods including pseudo-geneticmeta-knowledge, among other advantages.

In one aspect, an autonomous actor apparatus is provided. The autonomousactor apparatus includes a sensor configured to detect information aboutthe apparatus and an environment in which the apparatus is operating.The apparatus also includes a memory configured to store detectedinformation. The apparatus further includes a physical controllerconfigured to maintain a physical attribute of the apparatus. Thephysical attribute may be based on a control sequence for the apparatus.The physical attribute may be associated with a physical interaction ofthe apparatus with the environment. The apparatus also includes abehavior controller configured to maintain a behavior attribute of theapparatus. The behavior attribute may be based on a control sequence forthe apparatus. The apparatus also includes a processor. The processor ofthe apparatus may be configured to determine adjustments to the controlsequence, the determination based at least in part on the detectedinformation. The processor may be further configured to provide theadjustments to the physical controller and the behavior controller.

In another aspect, a method of generating a control sequence for anautonomous actor is provided. The method includes receiving one or morecontrol sequences from a plurality of autonomous actors. The methodincludes identifying portions of control sequences from the one or morecontrol sequences that will contribute to the control sequence for theautonomous actor based at least in part on a portion of the one or morereceived control sequences. The method also includes generating acontrol sequence for the autonomous actor based at least in part on theidentified control sequences.

In a further aspect, a computer-readable storage medium comprisinginstructions executable by a processor of an apparatus is provided. Theinstructions cause the apparatus to receive one or more controlsequences from a plurality of autonomous actors. The instructions alsocause the apparatus to identify portions of control sequences from theone or more control sequences that will contribute to the controlsequence for an autonomous actor based on at least a portion of the oneor more received control sequences. The instructions further cause theapparatus to generate a control sequence for the autonomous actor basedon at least the identified control sequences.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary system includingpseudo-genetic actors configured to consume meta-knowledge.

FIG. 2 is a block diagram illustrating an exemplary pseudo-geneticactor.

FIG. 3 is a block diagram illustrating an exemplary environmentcontroller.

FIG. 4 is a block diagram illustrating an exemplary representation of apseudo-gene.

FIG. 5 is a flowchart illustrating an exemplary method of reproductionfor a pseudo genetic actor.

FIG. 6 is a flowchart illustrating an exemplary method ofself-assessment.

FIG. 7 is a flowchart illustrating an exemplary method ofself-adaptation which may be included in a pseudo-genetic system.

FIG. 8 is a flowchart illustrating an exemplary method of generating acontrol sequence for pseudo genetic actors.

FIG. 9 is a flowchart illustrating a functional block diagram foranother exemplary pseudo-genetic actor.

DETAILED DESCRIPTION

Pseudo-genes and meta-knowledge may be implemented together orindependently to overcome several challenges with traditional artificialintelligence/genetic techniques. Systems and methods for artificiallyintelligent physical and virtual actors including pseudo-geneticinformation and configured to retain meta-knowledge are described. Theactors are adapted with unique behavioral and physical attributes whichmay independently evolve over time. The attributes may includereproduction based attributes which direct how, when, and with whom theactor reproduces and may be subject to mutation and reproductive forces.The attributes may include evaluation attributes which dictate when andhow each actor evaluates their performance. The evaluation attributesmay also be subject to mutation and reproductive forces. The attributesmay consider meta-knowledge which is a reflection of informationgathered by an actor. What and how much meta-knowledge is collected mayalso be expressed as one or more evolvable attributes.

Various aspects of the novel apparatuses and methods are described morefully hereinafter with reference to the accompanying drawings. Theapparatuses and methods described herein may, however, be implemented inmany different forms and should not be construed as limited to anyspecific structure or function presented throughout this disclosure.Rather, these aspects are provided so that this disclosure will bethorough and complete, and will fully convey the scope of the disclosureto those skilled in the art. Based on the teachings herein one skilledin the art should appreciate that the scope of the disclosure isintended to cover any aspect of the novel apparatuses and methodsdisclosed herein, whether implemented independently of or combined withany other aspect of the disclosure. For example, an apparatus may beimplemented or a method may be practiced using any number of the aspectsset forth herein. In addition, the scope of the description is intendedto cover such an apparatus or method which is practiced using otherstructure, functionality, or structure and functionality in addition toor other than the various aspects set forth herein. It should beunderstood that any aspect disclosed herein may be implemented by one ormore elements of a claim.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the disclosure.Although some benefits and advantages of the preferred aspects arementioned, the scope of the disclosure is not intended to be limited toparticular benefits, uses, or objectives. Rather, aspects of thedisclosure are intended to be broadly applicable to other evolutionbased systems or methods. The detailed description and drawings aremerely illustrative of the disclosure rather than limiting, the scope ofthe disclosure being defined by the appended claims and equivalentsthereof.

DEFINITIONS

In order to facilitate an understanding of the systems and methodsdiscussed herein, a number of terms are defined below. The terms definedbelow, as well as other terms used herein, should be construed toinclude the provided definitions, the ordinary and customary meaning ofthe terms, and/or any other implied meaning for the respective terms.Thus, the definitions below do not limit the meaning of these terms, butonly provide exemplary definitions.

A control sequence may refer to instructions for operating an actor,which may be provided by one or more entities. For example, a controlsequence may include the sequence of commands that may be used to move arobotic actor forward. The control sequence may include preconditionsand/or post-conditions for a sequence of commands. For example, themovement forward may be predicated on a determination that no obstacleis located in the desired movement direction. As another example, aftermovement, a robotic actor may be configured to pause operation to, forexample, allow a motor to cool down. The control sequence may be storedin static or semi-static memory. The control sequence may be stored as adata structure.

An example of a control sequence is a pseudo-gene. Pseudo-genes mayinclude representations of physical characteristics and behaviorcharacteristics of an actor.

An actor may include an entity, physical or virtual, programmed tointeract in an environment. The actor may be a computer opponent inonline poker (simulated environment), or a drone executing a complexseries of military maneuvers (real environment). Note that humans neednot directly participate in the simulated environment, e.g. a flightsimulator may simply test an actor's performance under harsh weatherconditions. An actor may include a simulated or virtual entitycontaining parallel components and/or capabilities as described herein.

Physical characteristics may include minimum or maximum speed or size,color, acceleration, or any other property indicating how an actorinteracts with a real or simulated environment. Physical characteristicsmay generally be associated with a physical interaction (e.g., movement)of an actor within its environment.

Behavior characteristics may include the likelihood of the actorchanging from one state to another, mating preferences, or thewillingness to communicate and/or remember meta-knowledge.

The state of an actor may generally refer to a collection ofmeasurements which an actor can determine from the environment at agiven time. A state change may include an actor changing location,posture, or self-assessment criteria. For example, in a video game,actors may be robot players whose characteristics are determined byand/or controlled by pseudo-genes. As the actors play iterations of thegame, some will perform better than others due to their geneticcomposition. Unlike traditional genetic representations of actors in apopulation—whereby all actors have the same physical characteristics andvary only at a behavior level—pseudo-genetic actors allow individualevolution of actors along physical and behavioral dimensionsconcurrently. Similarly, unlike traditional representations of actors ina population—whereby the performance rating and reproductive worthinessof all actors are judged by a single, universal rule—pseudo-geneticactors allow individual variations of performance rating andreproductive worthiness.

Meta-knowledge may include an effective expression of greaterintelligence based on the distilled information obtained from externalsources (e.g., observing other actors, extracting information from poolsof recorded data). For example, in a video game, meta-knowledge can beharvested from observing how human players pick a path through a givenarea, or calculating the statistically safest path by analyzing thesuccess and failure of human or robot players from a data pool.

By managing meta-knowledge, actors may capitalize on the wisdom of humanbehavior without having to mimic the workings of the human brain. Humansgenerally arrive at conclusions by creating a model of the entire arena,and then hypothesizing about a huge array of possible enemy positions,their line of sight, possible choke points, positions of allies andother enemies, recent troop movements, and numerous other variables.Humans then select the route with the highest chance of success. Inother words, the path chosen by the human player may be an expression ofintelligent modeling, based on volumes of experience and the ability toquickly hypothesize in a new environment.

Given this collection of interrelated variables, it would becomputationally difficult or impossible for a computer system tocalculate every possible enemy position on any given map in real-time,analyze possible choke points, weigh recent friendly and enemy troopmovements, etc. and then approximate a safe path to travel from Point Ato Point B. Instead, the actor can observe which paths are successfullytaken by humans (e.g., those that survive), and then imitate thisbehavior. This allows for rapid adaptation to new surroundings, andsaves thousands of “evolutionary” or “trial and error” cycles on thepart of the actor.

Within an artificial intelligence system, the pseudo-genes may be usedto control an actor's physical characteristics and/or alteration of thecharacteristics (e.g., the aspects of the actor which interact with areal or simulated environment). For instance, the pseudo-genes maycontrol the value function of an actor. The value function may evaluateand control various aspects of the actor. For example, the valuefunction may affect the collection, storage, and/or use ofmeta-knowledge. The value function may affect the activation of variousphysical attributes or capabilities (e.g., turning sensors on or off;altering power output to a propulsion system). The value function mayaffect the initiation of new structure or random behavior patterns. Thevalue function may be used to affect “social” interactions with otheractors such as sharing pseudo-genes or meta-knowledge information, orself-organizing with other actors into groups, working partnerships, ormating. Meta-knowledge may include recorded information regarding self(e.g., the actor), observed actors, environmental observations, and/orhistorical data archives including data. The value function and otherpseudo-genetic characteristics may be configured to determine the valueof the given meta-knowledge (e.g., on the whole), along with therelative value of each component of meta-knowledge.

Overview of Certain Advantages

One advantage of certain systems and methods described herein is thateach actor may inherit a unique set of physical and behavioralpseudo-genetic characteristics. The characteristics may then be slightlymutated. This can give rise to much more robust and diversesystems—tandem evolution of behavior and physical attributes can giverise to the necessity of complexity wherever appropriate for thephysical model and environment. This is done without contrived inputfrom simulation designers which may incorrectly estimate optimalphysical or behavioral characteristics.

Over time, successful lineages of actors may begin to specialize intoroughly defined “species.” This also allows for complementaryco-existence in a given environment. Some systems force physicallyidentical actors to compete for the exact same resource or target.However, in some implementations described below, the species canspecialize enough to allow for complementary behavior as well ascompetitive behavior. For example, one species of drones may specializein attacking targets in vehicles, while another may specialize intargeting targets on foot or under cover. While there can be overlapbetween the two, each species would complement the other as they huntedtargets. These non-trivial specializations would be the consequence ofprogrammatic evolution, not contrived templates created by programmersat the time of coding.

Another advantage of certain systems and methods described herein isthat each actor may have a unique, individual reward function. Thereward function parameters may be subject to learning adaptation andcontrolled by pseudo-genes. Accordingly, the reward function informationmay be passed to offspring and subject to mutation. Note that this caninclude a much more sophisticated method for attenuating historicalrewards as compared with a single exponential decay variable.

Because each actor may have a unique reward function, it may seek tomaximize its rewards in slightly different ways. In turn, these variedreward functions may complement different mutating physical orbehavioral pseudo-genes in each individual. This process can be boundedby a multitude of variables defined in each simulated environment. Thisis a sharp contrast to global reward functions which are applied to allactors in a given experiment, which by definition forces convergence toa narrow set of successful criteria. Diversity in reward functions maygive rise to very heterogeneous populations, which may self-adapt incomplex ways that would be unforeseeable by a simulation designer. Inother words, mutations in the reward function may encourage furthermutations which create actors more honed and successful in a givenenvironment than originally conceived by the designer when constructingthe simulation. Each surviving actor may present a unique set ofsolutions (behavioral and physical working in tandem) to the givenproblem, further refined by natural selection of highly successfulindividuals.

Note this is also very different than implementations which may allowactors to change the characteristics of their policies, but not alterthe reward functions. A policy generally refers to a set of rules orfunctions based on the environment state used to determine an action tobe taken by an actor. In such implementations, Policy (π1) may return agreater reward than Policy (π2)—however, the designer may haveincorrectly approximated the subtle final benefits expressed in theweights of the reward function, so that following Policy (π2) actuallygenerates much more desired results for the given scenario. Similarly,systemic boundaries or attributes may subtly change over the course of agiven simulation, especially if humans are also dynamically interactingwith the actors. Again, static-set reward functions and policies maycontinue to indicate that Policy (π1) is more favorable, even thoughPolicy (π2) might generate more desired results.

Again, designer-imposed parameters of the reward function are onlyarbitrary human guesses at the optimal desired result. The “true”parameters which dictate all possible valid configurations are theboundary conditions of the simulation (e.g. a value indicating overallhealth greater than 0, where health value of 0 indicates a “dead”actor).

When these artificial incentives dictated by the reward function areheld static, then the resultant actors will narrowly fulfill theexplicit reward system while remaining within the boundary conditions.This can be a huge weakness if the boundary conditions change or arehandled incorrectly (e.g., bad parameters of a simulation which issupposed to mimic real life), or if the conditions within theenvironment change enough so that the static reward function suddenlygenerates sub-optimal results. Given these conditions, in someimplementations, an actor may attempt to optimize itself according toskewed or incorrect parameters, and may collapse into a sub-optimalstate.

Several implementations described below encourage a much more broadexploration of configuration space. Accordingly, there are a much morebroad set of resultant actors to choose from. Similarly, if boundaryconditions change, there may be actors near the “edge” of theconfiguration space, which means they can more quickly adapt. Actors mayotherwise be too narrowly clustered near the universal, static rewardfunction variables to quickly explore new configurations.

Another advantage of certain systems and methods described herein isthat each actor may have individual criteria used for selectingdesirable reproduction partners (e.g., mates). This allows forincredible divergence in populations, since the criteria forreproduction may vary from individual to individual. This may give riseto highly heterogeneous actor populations which still meet the basiccriteria for “survival” and/or boundary conditions in a given simulatedenvironment, but can withstand incredible shocks or changes to theenvironment. The chances of a universal exploit arising in such adiverse population are greatly reduced.

Furthermore, this prevents rapid convergence to a single homogeneouscollection of actors. While this may somewhat slow the ability of thesimulated population to solve a single dilemma, the diversity createdfrom this reproductive schema leads to very robust heterogeneouspopulations which may not be vulnerable to any one exploit.

In addition, actors in some of the implementations described herein canform “working partnerships” or reproduce with any number of partners.The preference for the number of participating partners can becontrolled by pseudo-genes. The multiple-parent system and methodsdescribed may allow offspring to immediately benefit from thepseudo-gene and meta-knowledge information from all contributingparents.

Similarly, the pseudo-genetic or meta-knowledge contributions from eachparent may not be statically defined. Each parent may contribute alimited amount of pseudo-genetic or meta-knowledge information asdetermined by various evaluation functions which are unique to eachparent. This can prevent useless genes from having an equal chance ofbeing passed on as valuable genes. Parents may each calculate therelative importance of all pseudo-genetic or meta-knowledge materialbeing presented at the time of mating. In other words, each parent'sevaluation of the value of their own pseudo-genetic or meta-knowledgeand each reproductive partner's pseudo-genetic or meta-knowledgematerial can change over time. In addition to creating a greater varietyin offspring, which prevents convergence to localized extrema, this maycreate new offspring at each time of mating among a given group ofparents, rather than the same offspring every time.

Another advantage of certain systems and methods described is thatpseudo-genes may be used to alter the physical characteristics as wellas behavior characteristics. For example, there can be a “cost”associated with each Pseudo-Gene, which is used to change physicalcharacteristics or behavior characteristics. This “cost” for improvementor change can prevent a simulation system from collapsing into a trivialsolution state. This is critical for the system to find the perfect“trade off” between exploring new space for enhanced performance andexploiting known successful configurations. This also prevents collapseinto a trivial solution, e.g. always-perfect aim or infinite health.

For example, a simulation intended to test drones for militarymanufacture would have specific costs and boundaries on physicalattributes, such as movement speed, armor thickness, engine output,battery life, or any other property indicating how an actor interactswith a real or simulated environment. These costs and boundaries couldbe expressed in real dollar amounts. By introducing a spending limit tothe simulated environment (new boundary condition), the drone evolutionmay be directed to find the most economical trade-off between rewards(such as killing a target) and costs (in real dollars).

Note that this can also be applied to the process of evaluating newconfigurations—in other words, the system can assign a dollar cost totweak the configuration of a drone, which should parallel the real-lifemodification costs of changing manufacturing equipment. This creates acost structure which limits both the cost of equipment and cost ofmanufacturing new prototypes.

Furthermore, cost-balanced genetic improvements can implicitly showwhere the greatest improvements from research and development can bereaped—e.g., demonstrate specific, non-trivial new modifications todrones that can exponentially increase rewards (e.g., kills). Forexample, a simulation may show that a 10 percent increase in batterylife in tandem with a 5 percent increase in sensor range renders a10,000 percent improvement in drone kills. If limited research anddevelopment dollars are redirected towards these identifiedimprovements, it could generate a synergistic net improvement ineffectiveness and reduction in cost. In systems which do not necessarilyconsider an incremental “cost” for improvement, the simulation maycollapse to trivial or useless results, e.g. increase battery life orsensor range to infinity.

Example Network Environment

FIG. 1 is a block diagram illustrating an exemplary system includingpseudo-genetic actors configured to consume meta-knowledge. The system100 includes one or more actors such as actor 102 a, actor 102B, andactor 102C (individually or collectively herein referred to as actor102). In an implementation where the system 100 is simulating a processsuch as manufacturing a widget, each actor 102 may represent amanufacturer. In an implementation where the system 100 is simulating avirtual environment, such as first-person shooter video game, each actor102 may represent a player in the game. In another implementation theactors 102 may be physical objects such as robotic drones. Details of anactor 102 will be described in further detail below.

Each actor 102 of the system 100 may communicate with a network 104. Thecommunication may be bidirectional. The communication may beaccomplished via wired and/or wireless means. The communication may beprotocol-based such as via HTTP, 802.11, and the like. Each actor 102may be configured to communicate via a different communication path withthe network. The network 104 may include one or more cellular network, adata network, a local area network, a wide area network, a mesh network,the Internet, public switched telephone network, private network,satellite network, and/or other network.

The system 100 may include an environment controller 106 thatcommunicates with other devices via the network 104. In animplementation where the system 100 is simulating a process such asmanufacturing a widget, the environment controller 106 may represent theconstraints for the manufacturing process such as resource limits, speedof production, or other global boundary conditions. In an implementationwhere the system 100 is simulating a virtual environment such as afirst-person shooter video game, the environment controller 106 mayrepresent the entity responsible for enforcing global environment issuessuch as gravity, physics, health attributes, will, and the like. Inimplementation where the actors are physical machines, the environmentcontroller may represent a central server to provide global informationto the actors 102.

The system 100 may include an environment control data storage 108. Theenvironment control data storage 108 may be configured to storeinformation related to the environment. This information may be used viathe environment controller 106 to generate and enforce the environmentconditions for the system 100. The environment control data store may becoupled with the network 104. Accordingly, other entities of the system100, such as an actor 102, or the environment controller 106, may accessthe environment control data storage 108.

The environment control data storage 108 may include any suitable datastructure(s), such as one or more relational databases. In someimplementations the environment control data storage 108 may be amultidimensional database. In some implementations, the environmentcontrol data storage may be static. For example, the environment controldata storage 108 for a simulation may be held constant, to allowmultiple populations of actors to be generated using the sameenvironmental conditions. Some implementations of the system 100 mayprovide an interface to receive signals which may be stored in theenvironment control data storage 108 indicating various environmentcontrol data values. The environment control data values may beconsidered the boundary conditions for the simulation. For example, theenvironment control data value identifying the boundaries of a space inwhich actors are permitted to move may be included. Other environmentcontrol data values may include physics data (e.g., gravity, light),obstacle information (e.g., structures within the environment),processing resources for the simulation (e.g., in a virtual simulation,the capacity of the device(s) executing the simulation may placeconstraints on the environment), and the like.

The system 100 may also include a metadata storage 110. The metadatastorage 110 may be configured to store metadata about the system 100.For example in a system where the system 100 is simulating a processsuch as manufacturing a widget, the metadata may include informationsuch as number of widgets produced by each actor, quantity of inputsconsumed by each actor, relational information between the actors,evolutionary characteristics such as number of offspring, related to theactors, and the like. In an implementation where the system 100 issimulating a virtual environment such as a first-person shooter videogame, the metadata may include information such as health for eachactor, number of kills for each actor, number of deaths for each actor,evolutionary characteristics as described above, and the like. In animplementation where the actor 102 is a physical object such as arobotic drone, the metadata may include information such as location,path traveled, evolutionary characteristics such as those describedabove as well as resource levels such as fuel, ammunition, battery,power, and capabilities (e.g., surveillance capabilities, indicationcapabilities, data storage).

While the metadata has been described for separate implementations inslightly different terms, depending on the embodiment metadata mayinclude one or more of the above mentioned types of data. Metadata caninclude aggregated or calculated values based on the above describedcharacteristics. For instance, in a first-person shooter game, theammunition level may reflect an average ammunition level over a periodof time (e.g., actor's life). In a manufacturing simulation, the cost ofproduction may reflect an average cost of production where the system100 simulates a dynamic pricing model. Other aggregations andcalculations are contemplated.

Example Actor

FIG. 2 is a block diagram illustrating an exemplary pseudo-genetic actor102. The pseudo-genetic actor 102 may include a processor unit(s) 202.The processor unit(s) 202 may control operation of the actor 102. One ormore of the processor unit(s) 202 may be collectively referred to as acentral processing unit (CPU). Memory 206, which may include read-onlymemory (ROM) and/or random access memory (RAM), provides instructionsand data to the processor unit(s) 202. A portion of the memory 206 mayalso include non-volatile random access memory (NVRAM). The processorunit(s) 202 may be configured to perform logical and arithmeticoperations based on program instructions stored within the memory 206.The instructions in the memory 206 may be executable to implement themethods described herein. The processor may be configured to transmitsignals to one or more of the components of the actor 102. The processormay also be configured to receive signals from one or more of thecomponents of the actor 102.

The processor unit(s) 202 may be implemented with any combination ofgeneral or special purpose microprocessors, microcontrollers, digitalsignal processors (DSPs), field programmable gate array (FPGAs),programmable logic devices (PLDs), controllers, state machines, gatedlogic, discrete hardware components, dedicated hardware finite statemachines, or any other suitable entities that can perform calculationsor other manipulations of information.

In some implementations, such as robotic deployments, the actor 102 mayalso include machine-readable media for storing software. The processorunit(s) 202 may comprise one or more machine-readable media for storingsoftware. Software shall be construed broadly to mean any type ofinstructions, whether referred to as software, firmware, middleware,microcode, hardware description language, or otherwise. Instructions mayinclude code (e.g., in source code format, binary code format,executable code format, or any other suitable format of code). Theinstructions, when executed by the processor unit(s) 202, cause theactor 102 to perform various functions described herein.

The actor 102 may include one or more sensors 204. The sensor(s) 204 maybe used to receive information about the environment and/or other actorsin the system 100. In an implementation where the actor 102 is aphysical machine, such as a drone, the sensor(s) 204 may includecameras, clocks, antenna, accelerometers, gravimeters, radiometers,barometers, thermometers, microphones, infrared detectors, chemicalsensors, gyroscopes, photodetectors, SONAR, RADAR, and the like. Inimplementations where the actor 102 is a virtual actor, such as afirst-person shooter game, the sensor(s) 204 may include the “eyes” ofthe actor 102 that collect information that the virtual actor “sees.” Inimplementation where the system 100 is simulating a manufacturingprocess, the sensor(s) 204 may include throughput sensors, inputsensors, quality control sensors, and the like. Each of the sensor(s)204 may operate independently or be configured to communicate with othersensor(s) 204.

The sensor(s) 204 may be coupled with the processor unit(s) 202 toprovide data which can be used to affect control of the actor 102. Insome implementations, the sensor(s) 204 may store the sensed informationin a memory 206. Accordingly, the information may be provided to theprocessor unit(s) 202 via the memory 206. In some implementations, thesensor(s) 204 may provide information directly to the processor unit(s)202. The memory 206 may be a static memory, a dynamic memory, or aremovable memory. The memory may include a storage interface whichallows the received information to be organized into a structured formatsuch as a relational database.

The actor 102 may include a physical controller 208. The physicalcontroller 208 may be configured to express physically relatedpseudo-genes. For example, in implementation where a physical robot isthe actor 102, such as a drone, the physical controller 208 may includecomponents to adjust the size of the robot. For instance an actuator mayexpand the diameter of a robot thereby increasing the size of the actor.In virtual simulations, the physical controller 208 may be configured toadapt the physical characteristics of the virtual actor. For instance,the physical controller 208 may be configured to adjust the height andweight of an actor 102 in a first-person shooter game. In amanufacturing simulation system, the physical controller 208 may beconfigured to adjust physical characteristics of each manufacturingactor 102 such as size of plant, height of plant, physical plantattributes of manufacturing actor (e.g., sewage, electrical wiring, datanetworking, ventilation).

The physical controller 208 may be configured to communicate with theenvironment controller 106 (FIG. 1). For example, the physicalcontroller 208 may need to identify whether a particular physical actionis permitted by the environment. For example, if the actor 102 islocated against a wall, the physical action of “move forward” may not bepossible. By consulting with the environment controller 106, the actor102 via the physical controller 208 may obtain the necessary inputs tomanifest a given physical characteristic for the actor 102. Unlike othergenetic systems, the only boundary on a physical controller 208 actionmay be the global boundary conditions for the environment. The systemmay not include hardcoded constraints on each actor 102 which mayimplicitly limit the capabilities of a given actor and thus limit thesearch space available for genetic evolution.

The actor 102 may include a behavioral controller 210. The behavioralcontroller 210 may be configured to express behavior relatedpseudo-genes. For example, an implementation where a physical robot suchas a drone is contemplated, the behavioral controller 210 may includecomponents to adjust the flight pattern of the drone. For instance, aflight planning computer may alter a flight schedule for the dronethereby altering the behavior of the drone. In virtual simulations, thebehavioral controller 210 may be configured to adapt the behaviorcharacteristics of the virtual actor. For instance, the behavioralcontroller 210 may be configured to adjust the hunting pattern of afirst-person shooter actor 102 such as run around and seek targets orfind sniping location and wait. Other behavioral characteristics includeresponse to damage taken, response to damage delivered, response to newcapabilities, response to new information, quantity of information toretain, quantity of information to share, reproductive preferences, andthe like. In an implementation where the system 100 is simulating amanufacturing process, the behavioral controller 210 may be configuredto control attributes such as purchasing decisions, pricing decisions,output level decisions, number of workers, quantity and type ofequipment, and the like.

The actor 102 may include a metadata processor 212. The metadataprocessor may be configured to obtain, analyze, and/or create metadataassociated with the actor. The metadata processor 212 may be configuredto retrieve the metadata via the network 104 from the metadata storage110. The metadata processor 212 may be configured to calculate newmetadata values for an actor based on the metadata stored in themetadata storage 110. Information generated by the metadata processor212 may be stored in the memory 206 of the actor 102. In someimplementations, the metadata processor 212 may store the informationgenerated in the system 100 metadata storage 110.

The quantity of metadata stored and how the metadata is stored may becontrolled by the physical controller 208 and/or the behavior controller210 respectively. For example, the physical controller 208 may beconfigured to allow the actor 102 to have a memory of a specific size.The amount of metadata an actor may “remember” may thus be limited. Themetadata processor 212 may evolve to store certain metadata elements orsynthesize metadata from other metadata elements. Similarly, thebehavior controller 210 may identify a particular method of synthesizingmetadata. The metadata processor 212 may in turn execute this behaviorthereby generating a new synthetic item of metadata. The metadataprocessor 212 may store the information generated in the memory 206 ofthe actor 102.

The metadata processor 212 may also be configured to manage metadatacommunication between the actor 102 and other actors. For example, theactor 102 may be configured, such as via a behavior control attribute,such that the actor 102 should never communicate with other actors. Inthis situation, the actor 102 may configure the metadata processor 212to refuse to transmit metadata to other actors when requested.Alternatively, the actor 102 may be configured to transmit metadata withanyone who attempts to communicate with the actor 102. In this case, theactor 102 may be configured to transmit data to all corners.

The actor 102 may also include a genetic processor 214. The geneticprocessor 214 may be configured to manage and control pseudo-genes for agiven actor. The genetic processor 214 can be thought of as the DNA forthe actor 102. The genetic processor 214 may provide the pseudo-geneticrepresentation of the actor 102 to the physical controller 208 andbehavioral controller 210 for expression in the actor 102. The geneticprocessor 214 may store the pseudo-genes in the memory 206.

The above described components of the actor 102 may be coupled by a bus216. The bus 216 may be a data bus, communication bus, or other busmechanism to enable the various components of the actor 102 to changeinformation. It will further be appreciated that while differentelements have been shown, multiple processes may be combined into asingle element, such as a physical/behavioral controller, or combinationof processors into a single processor.

Example Environment Controller

FIG. 3 is a block diagram illustrating an exemplary environmentcontroller. The environment controller 106 includes a processor unit(s)302. The processor unit(s) 302 may control operation of the environmentcontroller 106. One or more of the processor unit(s) 302 may becollectively referred to as a central processing unit (CPU). As shown inFIG. 3, the environment controller 106 includes a memory 304. Memory304, which may include read-only memory (ROM) and/or random accessmemory (RAM), provides instructions and data to the processor unit(s)302. A portion of the memory 304 may also include non-volatile randomaccess memory (NVRAM).

The environment controller 106 may include a metadata collector 306. Themetadata collector 306 may be configured to collect metadata about theenvironment and/or the system 100. The metadata collector 306 may beconfigured to store the information collected in the metadata storage110. In an implementation where the system 100 is simulating afirst-person shooter, the metadata collector 306 may collect informationsuch as the location of actors, a status of actors (e.g., health,ammunition, stamina, skill, kills, deaths), and the like. In animplementation where the actor 102 is a physical machine, the metadatacollector 306 may collect information about the location of each actor,the physical actions of each actor, attributes of the environment forthe actor (e.g., weather, terrain, time), and the like. In someimplementations the metadata collector 306 may be coupled with one ormore sensors (not shown), the sensors configured to collect metadataabout the environment.

The environment controller 106 may also include an actor communicationsinterface 308. The actor communication interface 308 may be configuredto facilitate communication between individual and/or groups ofpseudo-genetic actors 102. For example, if an actor 102 wishes totransmit metadata to another actor in the system, the actor 102 maytransmit the message to the actor communications interface 308 which inturn may be configured to transmit the message to the recipient actor.In some implementations, the environment may communicate information tothe actors as a group. In this case, the environment controller 106 maytransmit a message to the actor communications interface 308 fordistribution to one or more actors. In an implementation where theactors are physical robots, the actor communications interface 308 maybe a radio access technology, or other network communication technology.

The environment controller 106 may also include a physics engine 310.The physics engine 310 may be used in systems where the environmentdetermines and/or maintains physical parameters of the environment suchas gravity, light, temperature, etc. The physics engine 310 is generallyconfigured to maintain boundary conditions for the environment. As aresult, the system 100 may be configured to allow each actor 102 toindividually evolve with as many degrees of freedom as possible withinan environment. As such, the physics engine 310 establishes theboundaries within which the actors may evolve.

The components of the environment controller 106 may be coupled by a bus314. The bus 314 may be a data bus, communication bus, or other busmechanism to enable the various components of the actor 102 to changeinformation. It will further be appreciated that while differentelements have been shown, multiple processes may be combined into asingle element, such as a physical/behavioral controller, or combinationof processors into a single processor.

Example Pseudo-Gene

FIG. 4 is a block diagram illustrating an exemplary representation of apseudo-gene. The pseudo-gene 400 may be used to represent the geneticinformation for an actor. Pseudo-genes are similar to biological genesas they are traits inherited from “parents” into new generations ofactors and are subject to random mutation. Pseudo-genes may control thephysical characteristics (e.g., maximum speed of movement) andbehavioral characteristics (e.g., percent chance to change from State Ato State B) of each actor. In a simulated environment, this may alterthe simulated physical characteristics of the actor; in a realenvironment, these mutations may be expressed as tweaks to design (e.g.,changing the ammunition load, fuel level, battery charge level, etc. ofa drone's physical body). Pseudo-genes may also control the reward whichan actor awards itself for a given environmental state.

As shown in FIG. 4, the sample pseudo-gene 400 includes two portions. Afirst portion 402 may be used to represent one or more behavioralattributes for the actor. The first portion 402 shown includes fourbehavioral attributes 402 a, 402 b, 402 c, and 402 d. The behavioralattributes may include information related to behavioral aspects of theactor. For example, values indicating how the actor should reproduce maybe included in the first portion. Other behavioral attributes which maybe indicated in the first portion 402 include the percent chance ofmoving from one environmental state to another or reproductive behavior(e.g., when, with whom, and how), for example.

By way of further examples, in an implementation where the system issimulating a process such as manufacturing a widget, behavior attributesmay include hire/fire workers, build/sell equipment, change supplier,change hours of operation, and the like. In an implementation where thesystem is simulating a virtual environment such as a first-personshooter video game, behavior attributes may include one or more actionsto take in response to an event or environment change (e.g., takingfire, direct hit, observe enemy, observe friendly), patterns of movement(e.g., path, speed, crouch, crawl, jump) and the like. In implementationwhere the actors are physical machines, the behavior attributes mayinclude one or more actions to take in response to a sensed event orenvironment change (e.g., detect weather change, observe object, impededforward motion), patterns of movement (e.g., path, speed), and the like.

The example pseudo-gene 400 also includes a second portion 404. Thesecond portion 404 may be used to represent one or more physicalattributes for the actor. The second portion 404 shown includes fourphysical attributes 404 a, 404 b, 404 c, and 404 d, which may indicateinformation related to physical aspects of the actor. For example,values indicating the height, width, weight, and/or total surface areafor an actor may be included in the second portion 404.

By way of example, in an implementation where the system is simulating aprocess such as manufacturing a widget, physical attributes may includephysical plant characteristics (e.g., utilities, property). In animplementation where the system is simulating a virtual environment suchas a first-person shooter video game, physical attributes may includeheight, weight, muscle, endurance, color, equipment, and the like. Inimplementation where the actors are physical machines, the physicalattributes may include dimensional data, equipment, color, and the like.

An example of a basic actor representable through pseudo-genes is anactor including one pseudo-gene. It should be noted that such an actormay not survive long in the target environment but may provide a pointof genetic diversity for future generations.

Evolution of Actors

Having described the system, the actors, and the pseudo-geneticinformation which drives the actors, the above described components canbe used to evolve actors over time. Evolution generally refers to theprocess whereby one or more actors contribute some genetic informationto yield one or more offspring (e.g., new actors). In someimplementations, the contribution patterns for a given actor arepre-defined as a function of an actor's fitness, a random genetic“cross-over” point, some random mutation rate, and/or other factors.However, in such implementations, the algorithm by which thereproduction occurs is static. Furthermore, the algorithm is applied toeach actor in the same way. Accordingly, the population may evolvetoward a local optimum point, while failing to provide the geneticdiversity to reach a globally optimal point.

FIG. 5 shows an exemplary method of reproduction for a pseudo geneticactor. The method shown in FIG. 5 may be performed by an actor, such asthe one shown in FIG. 2. In some implementations, the behaviorcontroller 210 may implement all or part of the method.

At block 502, an actor is identified as ready to reproduce. Theconditions triggering the readiness to reproduce may be represented inone or more behavioral attributes of a pseudo gene. In some actors, thebehavioral attribute for reproduction may include metadata. For example,the actor may be ready to reproduce when an environment metadata element(e.g., time), reaches a certain value (e.g., one day since lastreproduction). The actor may be ready to reproduce when an actormetadata element reaches a certain value such as actor health, actorlocation, actor achievement. A given actor may trigger reproductionbased on its own attributes or key off the attributes of another actor.The actor may be ready to reproduce when population metadata reachescertain values such as population statistics (e.g., average health,average lifespan). It should be emphasized that the consideration ofmetadata as well as the metadata elements considered may be evolvedattributes represented in the pseudo gene for an actor. Accordingly,this aspect of reproduction is not mandated by an algorithm, a priori,for all actors. Instead, the triggering condition for reproduction mayitself be the product of reproduction.

At block 504, additional reproduction behavioral attributes are obtainedfor the actor. In some implementations, it may be efficient to maintainonly the behavioral attributes related to the triggering of reproductionin active memory. Once triggered, the additional attribute(s) may beloaded at block 504. The additional attributes define other parametersthat influence how and with whom the actor will attempt reproduction.

At block 506, reproduction partner(s) are identified based on aselection attribute that may be part of the reproduction behavioralattributes obtained in block 504. The selection attribute defines thecriteria an actor will use to identify suitable partner(s). For example,the selection attribute may indicate reproduction with the first actorencountered. In other implementations, the actor may define selfreproduction whereby the actor will mate with itself. In someimplementations, the selection attribute may indicate multiplereproductive partners. As with the triggering of reproduction, theselection attribute may be driven by metadata. Furthermore, as above,which metadata elements and how the metadata elements are combined togenerate a selection attribute are themselves evolvable. Accordingly,the selection criteria may be unique for each actor and may evolve overtime.

At block 508, reproduction is performed. The process by whichreproduction is performed may also be unique for each actor. The processmay evolve over time as a product of reproduction with various actors.The reproduction, in its simplest form, may take certain pseudo genesfrom the actor and its partner(s) to generate a new actor.

It should be noted that in the pseudo genetic system described herein,unlike in the biological world and traditional genetic programs, thegene length may vary over time. Each actor in the system may include aunique number of genes as genetic information is contributed throughreproduction. For example, consider the case where an actor selects onepartner for reproduction. The actor has 10 pseudo genes and the partnerhas 12 pseudo genes. The actor may have evolved a method of reproductionwhereby each partner contributes half their genes. In such a case, theoffspring will have 11 genes (5 from the actor and 6 from the partner).In some implementations, a synthesizer may refer to the one or moreattributes indicating how the reproduction will be performed. That is,how the pseudo-genetic material selected from the partners will becombined to generate the offspring.

At decision block 510, a determination is made as to whether anymutations will occur for this reproduction. The determination may bebased at least in part on one or more pseudo-genes for the actor and/orpartner. The determination may also be based in part on information fromthe environmental controller. For example, in a virtual simulation, theenvironmental controller may include a radiation level which may beincluded in the determination of mutation for the actor and/or partner.The mutation determination may be performed for the actor or the pendingoffspring actors. Although shown as following block 508, in someimplementations, the determination regarding mutation may be performedprior to block 508.

If the determination is made to not mutate, the process continues toblock 512 where the offspring are added to the population. If thedetermination is made to mutate, at block 514, mutationcharacteristic(s) are identified. The identification of mutationcharacteristic(s) may be based at least in part on pseudo-genes for theactor. For example, the form of mutation, the quantity of mutation, thelocation of mutation, and the like, may be coded as pseudo-geneticattributes. The pseudo-genetic attributes may also consider metadatawhen determining the mutation characteristic(s).

At block 516, the mutation is performed based at least in part on theidentified mutation characteristic(s). For example, the mutation mayconsider a cost for mutating a particular attribute. In animplementation where the system is simulating a process such asmanufacturing a widget, each pseudo-gene may have a mutation costassociated with it. The quantity and amount of mutation may be limitedto a total cost for the actor. As should be apparent from thedescription above, this total cost may also be represented in thepseudo-gene for the actor. In some implementations, the mutation may bea defined as a random occurrence based on, for example, time. In someimplementations, the mutation determination may be performed once forthe entire pseudo-gene. In some implementations, the determination atblock 512 and subsequent actions at blocks 514 and 516 may be performedone or more time for each attribute represented in the pseudo-gene.

At block 512, the offspring resulting from the reproduction are added tothe population. The offspring are then considered actors within thesystem and may subsequently reproduce using a similar process as shownin FIG. 5.

Self-Assessment

In some genetic based systems, the assessment of each actor isperiodically achieved using a fitness test. The fitness test may be ageneralized assessment of actors to determine how well or poorly eachactor performed. This may provide a relative grading of actors whereinthe best actors have a higher fitness score than the worst actors. Insome systems, this fitness information is used to determine which actorssurvive and which are unfit to survive. In some implementations, thefitness function is static and does not change overtime. Furthermore, aswith the reproduction mechanism discussed above, in some systems thefitness function is a global assessment where all actors are judgedsimilarly. Accordingly, the population may evolve toward a local optimumpoint, while failing to provide the genetic diversity to reach aglobally optimal point.

FIG. 6 is a flowchart illustrating an example method of self-assessmentwhich allows actors to have unique self-assessments. The self-assessmentis governed by pseudo-genes and as such may mutate and evolve over time.The method shown in FIG. 6 may be implemented within an actor such asthat shown in FIG. 2.

At block 602, the actor determines whether it is ready to perform aself-assessment. The conditions triggering the readiness to self-assessmay be represented in one or more behavioral attributes of apseudo-gene. In some actors, the behavioral attribute forself-assessment may include metadata. For example, the actor may beready to self-assess when an environment metadata (e.g., time) elementreaches a certain value (e.g., 1 day since last self-assessment). Theactor may be ready to self-assess when an actor metadata element reachesa certain value such as actor health, actor location, or actorachievement, for example. A given actor may trigger self-assessmentbased on its own attributes or key off the attributes of another actor.The actor may be ready to self-assess when population metadata reachescertain values such as population statistics (e.g., average health,average lifespan). It should be emphasized that the consideration ofmetadata as well as the metadata elements considered may be evolvedattributes represented in the pseudo-gene for an actor. Accordingly,this aspect of self-assessment is not mandated by an algorithm, apriori, for all actors. Instead, the triggering condition forself-assessment may itself be the product of reproduction.

At block 604, the self-assessment behavioral attribute(s) are obtained.The self-assessment behavioral attribute(s) may include the basis forthe assessment (e.g., metadata to be included, which actors to includein the assessment). The self-assessment behavioral attribute(s) mayinclude a scope of the assessment (e.g., time, number of actors). Theself-assessment behavioral attribute(s) may include weights for theassessment. For example, in a first person shooter system, an actor mayevolve a self-assessment attribute whereby the number of kills by anactor is weighted more heavily in a self-assessment than the damageincurred by the actor. In a manufacturing simulation, theself-assessment for a given actor may weigh the quantity of pollutantsdispensed more heavily than the profits generated.

At block 606, the metadata identified as the basis for theself-assessment are obtained. In some implementations, this may includeretrieving metadata from the memory of the actor. In someimplementations, this may include retrieving metadata from theenvironment controller. In some implementations, this may also includeretrieving metadata from other actors.

At block 608, a determination is made as to whether the actor willsurvive. The actor may determine based on its evolved self-assessmentparameters that it is no longer fit to survive. At block 610, the actorinitiates termination procedures. As part of the termination procedures,the actor may be configured to transmit a signal to the environmentcontroller (e.g., population controller) indicating that the actor isremoving itself from the population. In this case, the actor may destroyor otherwise remove itself from the environment. In a simulatedenvironment, this may include freeing the memory locations representingthe actor. In a real environment, this may include transmitting a signalto cause the destruction of the physical actor. For example, a robot mayinclude an explosive charge which, when signaled, will render the robotinoperable. If the assessment determines the robot has sustained toomuch damage to be useful or has strayed beyond its operationalboundaries, the self-assessment may render the determination that therobot should be destroyed. Accordingly, a signal may be transmitted tothe charge to detonate the explosive charge. At block 612, the actorterminates further processing.

Returning to decision block 608, if the assessment determines the actorsurvives, at block 614, a determination is made as to whether to alterthe actor's current policy. As with self-assessment, the determinationas to whether to alter the current policy may be controlled bypseudo-genes. For example, the determination may be based onmeta-knowledge such as historical information about the actor. In afirst-person-shooter example, the determination may include kill trendssuch that as the number of kills for a given period of time is trendingup, the policy may not change, but if the kills are trending down, thepolicy may change. Each actor is associated with a policy that dictateshow the pseudo-genetic information is organized and processed. A policymay be the probability for an actor to take a certain action in a givenstate. As with reproduction or survival, the policy may be representedin the pseudo-gene for the actor and evolve over time.

At block 612, as a result of the self-assessment, the policy may bealtered. If the assessment indicates the current policy does not needaltering, the current policy is maintained at block 616. The actorcontinues processing at block 618. Returning to decision block 614, ifthe assessment indicates the policy should be altered, the currentpolicy is altered at block 620. Altering the current policy may includechanging the probabilities represented in the pseudo-gene for the actorwhich indicate the likelihood an actor will take a certain action in agiven state. In some implementations, altering the policy may includemutating the pseudo-gene for the actor as described above. The alteredcurrent policy is then used to continue processing for this actor atblock 618.

FIG. 7 is a flowchart illustrating an exemplary method ofself-adaptation which may be included in a pseudo-genetic system.Several advantages may be provided by the method of self-adaptationshown in FIG. 7. For example, the method may provide the ability to testa wide range of physical and behavioral characteristics embodied indifferent actors, concurrently. This can create a much more diverse,robust system. Furthermore, changes to the environment may favor onevariant over the other, but there is no single physical profile whichcan be universally compromised from a single flaw or weakness. Inaccordance with the process shown in FIG. 7 and described in detailabove, each actor can inherit a unique set of physical and behavioralpseudo genetic characteristics, which may be mutated over time. This maygive rise to much more robust and diverse systems—tandem evolution ofbehavior and physical attributes can give rise to the necessity ofcomplexity wherever appropriate for the physical model and environment.This may be done without contrived input from programmers which mayincorrectly estimate optimal physical or behavioral characteristics.

Over time, successful lineages of actors may begin to specialize intoroughly defined “Species.” This may also allow for complementaryco-existence in a given environment. In some implementations, physicallyidentical actors compete for the exact same resource or target. However,by providing pseudo-genetic species, the actors can specialize enough toallow for complementary behavior as well as competitive behavior. Forexample, one species of drones may specialize in attacking targets invehicles, while the other may specialize in targeting targets on foot orunder cover. While there can be overlap between the two, each specieswould complement the other as they hunted targets. These non-trivialspecializations would be the consequence of programmatic evolution, notcontrived templates created by programmers at the time of coding.

The process takes as inputs, one or more pseudo-genetic actors. Theprocess shown in FIG. 7 includes three pseudo-genetic actors 102 a, 102b, and 102 n. It will be appreciated that n may represent any number ofactors that are supported by the system. Each pseudo-genetic actorincludes a pseudo-gene 400 and meta-knowledge 780. The pseudo-gene 400may be implemented as described above in FIG. 4. The meta-knowledge 780includes the information retained by the actor 102. The information maybe information about the actor, about other actors, and/or about theenvironment. The information may include one or more of currently sensedinformation, historical information, and predicted information. Themeta-knowledge retained by a given actor is dependent on one or moreattributes included in the pseudo-gene for the actor. For example, oneactor may be configured to only remember what happened to the actor onthe previous simulation run. In a first person shooter simulation, thismay be performance statistics (e.g., accuracy, hits, health, distancetraveled) for the previous match. The amount of retained meta-knowledgethat is shared with other actors may also be dependent on one or moreattributed included in the pseudo-gene for the actor. For example, in amanufacturing simulation, a manufacturing actor may evolve to share itscost of inputs parameter with other actors while preventing access toprofit data.

At block 702, a simulation run is initiated. The simulation run mayinclude individually driven breeding and meta-knowledge sharing asdescribed above. The simulation may include providing information to theactors 102 individually or collectively.

At block 704, a system snapshot is measured. The system snapshot mayinclude an overall assessment of the simulation. The system snapshot mayalso include individual actor information.

Using the snapshot information measured at block 704, a determination ismade at decision block 706 as to whether the desired results areobtained. For example, the system snapshot desired result may be basedon a comparison of actors. In a manufacturing simulation, the comparisonmay be cost, profit, and output for each actor. The decision block 706may assess the population performance as a whole. For example, in amanufacturing simulation, the snapshot may seek to identify aconfiguration of actors that produces a certain level of total outputfor a given level of inputs.

It should be noted that while the system snapshot is a holisticassessment of the population, the overall evolution of the system is notdependent upon the result of the snapshot. Each actor is independent toevolve during the simulation run. Furthermore, it should be noted thatthe snapshot indicates a moment in time. While the snapshot is assessed,the simulation may continue running and actors may continue evolving.

If the desired results are obtained, the result details may be output atblock 708. The output may include transmitting one or more signalsindicating the desired result has been achieved, the details of thepopulation and individual actors leading to the result, and/or otherinformation regarding the simulation such as number of iterationsexecuted, time for processing, and statistical information regarding thepopulation (e.g., over time). The output may cause a visual display torender the details.

Returning to decision block 706, if the desired result has not beenobtained, the flow may return to execute another simulation run at block702 as describe above.

Control Sequences

FIG. 8 is a flowchart illustrating an exemplary method of generating acontrol sequence for pseudo genetic actors. The method of FIG. 8 may beimplemented in one or more of the devices described herein, such as thatshown in FIG. 2.

At block 802, one or more control sequences from a plurality ofautonomous actors are received. The control sequences may bepseudo-genes as described, for example, in FIG. 4. At block 804, controlsequences from the one or more control sequences that will contribute tothe control sequence for the autonomous actor are identified. Theidentification is based on a portion of the one or more received controlsequences. For example, as described above, the reproduction behaviorattributes of a pseudo-gene for an actor may used to identify thecontrol sequence for generating new actors. At block 806, a controlsequence for the autonomous actor based at least in part on theidentified control sequences is generated. This may includeidentification of what portion of the pseudo-genes are contributed fromeach actor as well as any mutation that may occur.

FIG. 9 is a block diagram illustrating another exemplary pseudo-geneticactor. The exemplary pseudo-genetic actor 900 may be configured toimplement one or more of the methods described above. The pseudo-geneticactor 900 may be a physically instantiated actor (e.g., robot) or avirtual instantiation (e.g., manufacturing simulation, first personshooter simulation). If implemented virtually, the pseudo-genetic actor900 may be represented in specialized circuitry, adapted to thesimulated environment. Those skilled in the art will appreciate that apseudo-genetic actor may have fewer or additional components than thesimplified pseudo-genetic actor 900 shown in FIG. 9. The pseudo-geneticactor 900 includes a detecting circuit 902, a storage 904, a physicalattribute controller 906, a behavior attribute controller 908, and anattribute adjustment controller 910.

In some implementations, the detecting circuit 902 is configured todetect information about the apparatus and an environment in which theapparatus is operating. The detecting circuit 902 may include one ormore of a sensor, a memory, and a processor. In some implementations,the means for detecting information about the apparatus and anenvironment in which the apparatus is operating include the detectingcircuit 902.

In some implementations, the storage 904 is configured to store thedetected information. The storage 904 may include one or more of staticmemory, RAM, disk, and network storage. In some implementations, themeans for storing detected information include the storage 904.

In some implementations, the physical attribute controller 906 isconfigured to maintain the physical attributes of the apparatus. In aphysical instantiated actor, the physical attribute controller 906 mayinclude actuators, pistons, hydraulics, and a processor. In a virtualinstantiated actor, the physical attribute controller 906 may include aprocessor and a physical controller. In some implementations, the meansfor maintaining a physical attribute of the apparatus include thephysical attribute controller 906.

In some implementations, the behavior attribute controller 908 isconfigured to maintain a behavior attribute of the apparatus. Thebehavior attribute controller 908 may include one or more of aprocessor, a memory, and a sensor. In some implementations, the meansfor maintaining a behavior attribute of the apparatus include thebehavior attribute controller.

In some implementations, the attribute adjustment controller 910 isconfigured to determine adjustments a physical attribute and a behaviorattribute based in part on the detected information and provide theadjustments to the physical controller and the behavior controller. Theattribute adjustment controller 910 may include a processor, a memory,and a bus. In some implementations, the means for determiningadjustments may include the attribute adjustment controller 910.

Other

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the Figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array signal (FPGA) or other programmable logic device(PLD), discrete gate or transistor logic, discrete hardware componentsor any combination thereof designed to perform the functions describedherein. A general purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

In one or more aspects, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Also, any connectionis properly termed a computer-readable medium. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared, radio,and microwave, then the coaxial cable, fiber optic cable, twisted pair,DSL, or wireless technologies such as infrared, radio, and microwave areincluded in the definition of medium. Disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and blu-ray disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Thus, in some aspects computer readable medium may comprisenon-transitory computer readable medium (e.g., tangible media). Inaddition, in some aspects computer readable medium may comprisetransitory computer readable medium (e.g., a signal). Combinations ofthe above should also be included within the scope of computer-readablemedia.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware or any combination thereof. If implemented in software, thefunctions may be stored as one or more instructions on acomputer-readable medium. A storage media may be any available mediathat can be accessed by a computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Disk and disc, asused herein, include compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and Blu-ray® disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a device as applicable. Forexample, such a device can be coupled to a server to facilitate thetransfer of means for performing the methods described herein.Alternatively, various methods described herein can be provided viastorage means (e.g., RAM, ROM, a physical storage medium such as acompact disc (CD) or floppy disk, etc.), such that a device can obtainthe various methods upon coupling or providing the storage means to thedevice. Moreover, any other suitable technique for providing the methodsand techniques described herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

While the foregoing is directed to aspects of the present disclosure,other and further aspects of the disclosure may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

1. (canceled)
 2. A system for managing an autonomous actor network, the system comprising: a metadata collector configured to obtain metadata information associated with the autonomous actor network and autonomous actors included therein; an autonomous actor, said autonomous actor including a self-assessment identifier indicative of: metadata elements stored by the autonomous actor, a quantity of metadata information for each metadata element stored by the autonomous actor, and a relationship between the metadata elements for the autonomous actor and the quantity of metadata information stored for each metadata element; and an assessment processor configured to generate an assessment value for the autonomous actor based on the obtained metadata information and the self-assessment identifier.
 3. The system of claim 2, wherein the metadata information associated with the autonomous actor network comprises at least one of network size, time, physical parameter, or boundary condition for the autonomous actor network.
 4. The system of claim 2, wherein metadata information associated with autonomous actors comprises one or more of: a measurement of a height, a width, a weight, a total surface area, a speed, an armor thickness, an engine output, a battery life, color, and a property indicating how the actor interacts with the autonomous actor network; a classification based at least in part on the measurement; or a calculation based at least in part on the measurement.
 5. The system of claim 2, wherein the self-assessment identifier comprises a pseudo-gene.
 6. The system of claim 2, wherein the relationship identifies a process for combining the indicated metadata elements.
 7. The system of claim 6, wherein the relationship includes a threshold result, and wherein generating the assessment value for the autonomous actor comprises comparing the threshold result to a combination of the metadata elements for the autonomous actor and the quantity of metadata information stored for each metadata element in accordance with the identified process.
 8. The system of claim 2, further comprising a genetic processor configured to alter the self-assessment identifier for said autonomous actor based in part on the assessment value.
 9. The system of claim 8, wherein altering said self-assessment identifier comprises at least one of changing the metadata elements for the autonomous actor, the quantity of metadata information stored by the autonomous actor, or the relationship between the metadata elements for the autonomous actor and the quantity of metadata information stored for each metadata element.
 10. The system of claim 8, wherein said altering is based on a control-sequence including one or more values indicating which actors of the plurality of autonomous actors will contribute to the self-assessment identifier and a contribution from each of the indicated actors.
 11. The system of claim 2, wherein the autonomous actor comprises a physical device having a memory configured to store the self-assessment identifier.
 12. The system of claim 2, wherein the autonomous actor comprises a virtual actor instantiated by an electronic device, the virtual actor configured to store the self-assessment identifier in a portion of a memory in communication with the electronic device.
 13. The system of claim 2, wherein the assessment value identifies a quantity of an overall goal achieved by the associated autonomous actor.
 14. A method of managing an autonomous actor network, the method comprising: obtaining, by a network management computing system comprising computer hardware, metadata information associated with the autonomous actor network and autonomous actors included therein; associating, by the network management computing system, a self-assessment identifier with an autonomous actor, the self-assessment identifier indicative of: metadata elements stored by the autonomous actor, a quantity of metadata information for each metadata element stored by the autonomous actor, and a relationship between the metadata elements for the autonomous actor and the quantity of metadata information stored for each metadata element; and generating, by the network management computing system, an assessment value for the autonomous actor based on the obtained information and the self-assessment identifier.
 15. The method of claim 14, wherein the self-assessment identifier comprises a pseudo-gene.
 16. The method of claim 14, wherein the relationship identifies a process for combining the indicated metadata elements.
 17. The method of claim 16, wherein the relationship includes a threshold result, and wherein generating the assessment value for the autonomous actor comprises comparing the threshold result to a combination of the metadata elements for the autonomous actor and the quantity of metadata information stored for each metadata element in accordance with the identified process.
 18. The method of claim 14, further comprising altering the self-assessment identifier for said autonomous actor based in part on the assessment value.
 19. The method of claim 18, wherein altering said self-assessment identifier comprises at least one of changing the metadata elements for the autonomous actor, the quantity of metadata information stored by the autonomous actor, or the relationship between the metadata elements for the autonomous actor and the quantity of metadata information stored for each metadata element.
 20. The method of claim 18, wherein the altering is based on a control-sequence including one or more values indicating which actors of the plurality of autonomous actors will contribute to the self-assessment identifier and a contribution from each of the indicated actors.
 21. A tangible computer-readable storage medium comprising instructions executable by a processor of an apparatus to cause the apparatus to: obtain metadata information associated with an actor network and autonomous actors included therein; associate a self-assessment identifier with an autonomous actor, the self-assessment identifier indicative of: metadata elements stored by the autonomous actor, a quantity of metadata information for each metadata element stored by the autonomous actor, and a relationship between the metadata elements for the autonomous actor and the quantity of metadata information stored for each metadata element; and generate an assessment value for the autonomous actor based on the obtained information and the self-assessment identifier. 